Year: 2016
Type: article
Abstract: Career growth in academia is often dependent on student reviews of university profes- sors. A growing concern is how evaluation of teaching has been affected by gender biases throughout the reviewing process. However, pinpointing the exact causes and consequen- tial effects of this form of gender inequality has been a hard task. Current work focusses on university-wide student reviewing system, that depends on objective responses on a Likert scale to measure various aspects of an instructor’s qual- ity. Through our work, we access online student review data which are not limited by geographies, universities, or disciplines. Thereafter, we come up with a systematic approach to assess the various ways in which gender inequality is apparent from the student reviews. We also suggest a possible way in which bias related to the gender of a professor could be detected from both objective numerical measures and subjective opinions in reviews. Finally, we assess a logistic re- gression learning algorithm to find the most important factors that can help in identifying gender inequality.
Author: Souradeep Sinha
Institution: Syracuse University
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2482704368